Co-Variability between the Surface Wind Divergence and Vorticity over the Ocean

Abstract

We examine the co-variability between the surface wind divergence and vorticity and how it varies with latitude in the Pacific Ocean using surface vector winds from reanalysis and satellite scatterometer observations. We show a strong correlation between divergence and vorticity throughout the extratropical oceans. From this observation, we develop a dynamical model to explain the first-order dynamics which govern this strong co-variability. Our model exploits the fact that for much of the time, the large-scale surface winds are approximately in a steady-state Ekman balance to first order. An angle α′ is derived from Ekman dynamics by utilizing only the surface divergence and vorticity and is shown to succinctly summarize the co-variability between divergence and vorticity. This approach yields insight into the dynamics that shape the spatial variations in the large-scale surface wind field over the ocean; previous research has focused mainly on explaining variability in the vector winds rather than the derivative wind fields. Our model predicts two steady-state conditions which are easily identifiable as discrete peaks in α′ Probability Distribution Functions (PDFs). In the Northern Hemisphere, steady-state conditions can be either (1) diverging, with negative vorticity, or (2) converging, with positive vorticity. We show that these two states correspond to relative high and low sea-level pressure features, respectively. Southern Hemisphere conditions are similar to those of the Northern Hemisphere, except with the opposite sign of vorticity. This model also predicts the latitudinal variations in the co-variability between divergence and vorticity due to the latitudinal variation in the Coriolis parameter. The main conclusion of this study is that the statistical co-variability between the surface divergence and vorticity over the ocean is consistent with Ekman dynamics and provides perhaps the first dynamical approach for interpreting their statistical distributions. The related α′ PDFs provide a unique method for analyzing air–sea interactions and will likely have applications in evaluating the surface wind fields from scatterometers and weather and reanalysis models.